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基于小波包能量的煤矿瓦斯和煤尘爆炸声音识别方法
Authors
余星辰
王云泉
Publication date
1 February 2023
Publisher
Editorial Department of Industry and Mine Automation
Doi
Cite
Abstract
针对目前煤矿瓦斯和煤尘爆炸监测漏报率和误报率高,难以满足瓦斯和煤尘爆炸事故应急救援需求的问题,提出了一种基于小波包能量的煤矿瓦斯和煤尘爆炸声音识别方法。在煤矿井下重点监测区域安装矿用拾音器,实时采集煤矿井下设备工作声音及环境音等;通过小波包分解提取声音的小波包能量占比,构成表征声音信号的特征向量;将特征向量输入BP神经网络中,训练得到煤矿瓦斯和煤尘爆炸声音识别模型;提取待测声音信号的小波包能量占比,并构成特征向量输入模型中,识别待测声音信号的类型。根据特征向量和输出结果要求,建立了输入层、隐含层和输出层节点数分别为8,8,1的BP神经网络用于识别模型的训练;通过分析煤矿井下声音信号小波包分解结果,确立了采用Haar小波函数,选择小波包分解层数为3。实验结果表明:瓦斯和煤尘爆炸声音通过小波包分解后的能量占比与其他声音差异明显,且不同时长的同一声音信号的小波包能量占比分布稳定,因此小波包能量占比可有效表征声音信号特征,且具有较强的鲁棒性;BP神经网络训练速度快,仅需较少的训练迭代次数就能达到期望误差,且在煤矿井下众多干扰声音信号存在的情况下识别准确率达95%,与极限学习机模型、支持向量机模型相比,BP神经网络识别效果最优
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Last time updated on 08/03/2023